task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Stereo Depth Estimation | KITTI2015 | 3D-MobileStereoNet | https://arxiv.org/abs/2108.09770v1 | three pixel error | 1.69 |
Stereo Depth Estimation | KITTI2015 | CDN-GANet Deep | https://arxiv.org/abs/2007.03085v2 | three pixel error | 1.92 |
Stereo Depth Estimation | KITTI2015 | HITNET | https://arxiv.org/abs/2007.12140v5 | three pixel error | 2.43 |
Stereo Depth Estimation | KITTI2015 | 2D-MobileStereoNet | https://arxiv.org/abs/2108.09770v1 | three pixel error | 2.67 |
Stereo Depth Estimation | KITTI2015 | TriStereoNet | https://arxiv.org/abs/2111.12502v2 | D1-all All | 2.35 |
Stereo Depth Estimation | KITTI2015 | TriStereoNet | https://arxiv.org/abs/2111.12502v2 | D1-all Noc | 2.09 |
Stereo Depth Estimation | KITTI2015 | ChiTransformer | http://openaccess.thecvf.com//content/CVPR2022/html/Su_Chitransformer_Towards_Reliable_Stereo_From_Cues_CVPR_2022_paper.html | D1-all All | 2.60 (self-sup.) |
Stereo Depth Estimation | KITTI2015 | ChiTransformer | http://openaccess.thecvf.com//content/CVPR2022/html/Su_Chitransformer_Towards_Reliable_Stereo_From_Cues_CVPR_2022_paper.html | D1-all Noc | 2.38 (self-sup.) |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | DFI-OmniStereo | https://arxiv.org/abs/2503.23502v1 | Depth-MAE | 1.463 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | DFI-OmniStereo | https://arxiv.org/abs/2503.23502v1 | Depth-RMSE | 3.767 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | DFI-OmniStereo | https://arxiv.org/abs/2503.23502v1 | Depth-MARE | 0.108 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | DFI-OmniStereo | https://arxiv.org/abs/2503.23502v1 | Depth-LRCE | 0.397 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | DFI-OmniStereo | https://arxiv.org/abs/2503.23502v1 | Disp-MAE | 0.158 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | DFI-OmniStereo | https://arxiv.org/abs/2503.23502v1 | Disp-RMSE | 0.338 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | DFI-OmniStereo | https://arxiv.org/abs/2503.23502v1 | Disp-MARE | 0.120 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | DFI-OmniStereo | https://arxiv.org/abs/2503.23502v1 | Disp-LRCE | 0.058 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360-IGEV-Stereo | https://arxiv.org/abs/2411.18335v2 | Depth-MAE | 1.720 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360-IGEV-Stereo | https://arxiv.org/abs/2411.18335v2 | Depth-RMSE | 4.297 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360-IGEV-Stereo | https://arxiv.org/abs/2411.18335v2 | Depth-MARE | 0.130 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360-IGEV-Stereo | https://arxiv.org/abs/2411.18335v2 | Depth-LRCE | 0.388 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360-IGEV-Stereo | https://arxiv.org/abs/2411.18335v2 | Disp-MAE | 0.188 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360-IGEV-Stereo | https://arxiv.org/abs/2411.18335v2 | Disp-RMSE | 0.404 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360-IGEV-Stereo | https://arxiv.org/abs/2411.18335v2 | Disp-MARE | 0.146 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360-IGEV-Stereo | https://arxiv.org/abs/2411.18335v2 | Disp-LRCE | 0.054 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | IGEV-Stereo | https://arxiv.org/abs/2303.06615v2 | Depth-MAE | 1.860 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | IGEV-Stereo | https://arxiv.org/abs/2303.06615v2 | Depth-RMSE | 4.474 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | IGEV-Stereo | https://arxiv.org/abs/2303.06615v2 | Depth-MARE | 0.146 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | IGEV-Stereo | https://arxiv.org/abs/2303.06615v2 | Depth-LRCE | 1.203 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | IGEV-Stereo | https://arxiv.org/abs/2303.06615v2 | Disp-MAE | 0.225 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | IGEV-Stereo | https://arxiv.org/abs/2303.06615v2 | Disp-RMSE | 0.423 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | IGEV-Stereo | https://arxiv.org/abs/2303.06615v2 | Disp-MARE | 0.172 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360SD-Net | https://arxiv.org/abs/1911.04460v2 | Depth-MAE | 2.112 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360SD-Net | https://arxiv.org/abs/1911.04460v2 | Depth-RMSE | 5.077 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360SD-Net | https://arxiv.org/abs/1911.04460v2 | Depth-MARE | 0.152 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360SD-Net | https://arxiv.org/abs/1911.04460v2 | Depth-LRCE | 0.904 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360SD-Net | https://arxiv.org/abs/1911.04460v2 | Disp-MAE | 0.224 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360SD-Net | https://arxiv.org/abs/1911.04460v2 | Disp-RMSE | 0.419 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | 360SD-Net | https://arxiv.org/abs/1911.04460v2 | Disp-MARE | 0.191 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | http://arxiv.org/abs/1803.08669v1 | Depth-MAE | 2.509 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | http://arxiv.org/abs/1803.08669v1 | Depth-RMSE | 5.673 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | http://arxiv.org/abs/1803.08669v1 | Depth-MARE | 0.176 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | http://arxiv.org/abs/1803.08669v1 | Depth-LRCE | 1.809 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | http://arxiv.org/abs/1803.08669v1 | Disp-MAE | 0.286 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | http://arxiv.org/abs/1803.08669v1 | Disp-RMSE | 0.496 |
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation | Helvipad | PSMNet | http://arxiv.org/abs/1803.08669v1 | Disp-MARE | 0.248 |
Age And Gender Classification | BN-AuthProf | Multinomial Naive Bayes (MNB) | https://arxiv.org/abs/2412.02058v1 | F1 score | 0.905 |
Age And Gender Classification | Adience Gender | MiVOLO-V2 | https://arxiv.org/abs/2403.02302v4 | Accuracy (5-fold) | 97.39 |
Age And Gender Classification | Adience Gender | ViT-hSeq | https://arxiv.org/abs/2403.12483v2 | Accuracy (5-fold) | 96.56 |
Age And Gender Classification | Adience Gender | MiVOLO-D1 | https://arxiv.org/abs/2307.04616v2 | Accuracy (5-fold) | 96.51 |
Age And Gender Classification | Adience Gender | RetinaFace + ArcFace + MLP + Skip connections | https://arxiv.org/abs/2108.08186v2 | Accuracy (5-fold) | 90.66 |
Age And Gender Classification | Adience Gender | CPG (single crop, pytorch) | https://arxiv.org/abs/1910.06562v3 | Accuracy (5-fold) | 89.66 |
Age And Gender Classification | Adience Gender | PAENet (single crop, tensorflow) | https://dl.acm.org/doi/10.1145/3323873.3325053 | Accuracy (5-fold) | 89.08 |
Age And Gender Classification | Adience Gender | Levi_Hassner CNN ( over-sample, caffe) | https://talhassner.github.io/home/publication/2015_CVPR | Accuracy (5-fold) | 86.8 |
Age And Gender Classification | Adience Gender | Levi_Hassner CNN (single crop, caffe) | https://talhassner.github.io/home/publication/2015_CVPR | Accuracy (5-fold) | 85.9 |
Age And Gender Classification | Adience Gender | LMTCNN-2-1 (single crop, tensorflow) | http://arxiv.org/abs/1806.02023v1 | Accuracy (5-fold) | 85.16 |
Age And Gender Classification | Adience Gender | Levi_Hassner CNN (single crop, tensorflow) | https://talhassner.github.io/home/publication/2015_CVPR | Accuracy (5-fold) | 82.52 |
Age And Gender Classification | Adience Age | ViT-hSeq | https://arxiv.org/abs/2403.12483v2 | Accuracy (5-fold) | 84.91 |
Age And Gender Classification | Adience Age | MiVOLO-V2 | https://arxiv.org/abs/2403.02302v4 | Accuracy (5-fold) | 69.43 |
Age And Gender Classification | Adience Age | MiVOLO-D1 | https://arxiv.org/abs/2307.04616v2 | Accuracy (5-fold) | 68.69 |
Age And Gender Classification | Adience Age | AL-ResNets-34 + IMDB-WIKI | https://arxiv.org/abs/1805.10445v2 | Accuracy (5-fold) | 67.47 |
Age And Gender Classification | Adience Age | R-SAAFc2 +IMDB-WIKI | http://proceedings.mlr.press/v54/hou17a.html | Accuracy (5-fold) | 67.3 |
Age And Gender Classification | Adience Age | RoR-34 + IMDB-WIKI | http://arxiv.org/abs/1710.02985v1 | Accuracy (5-fold) | 66.74 |
Age And Gender Classification | Adience Age | MWR | https://arxiv.org/abs/2203.13122v1 | Accuracy (5-fold) | 62.6 |
Age And Gender Classification | Adience Age | UNIORD-ResNet-101 (single crop, pytorch) | https://arxiv.org/abs/2011.07607v2 | Accuracy (5-fold) | 61 |
Age And Gender Classification | Adience Age | RetinaFace + ArcFace + MLP + IC + Skip connections | https://arxiv.org/abs/2108.08186v2 | Accuracy (5-fold) | 60.86 |
Age And Gender Classification | Adience Age | CPG (single crop, pytorch) | https://arxiv.org/abs/1910.06562v3 | Accuracy (5-fold) | 57.66 |
Age And Gender Classification | Adience Age | PAENet (single crop, tensorflow) | https://dl.acm.org/doi/10.1145/3323873.3325053 | Accuracy (5-fold) | 57.3 |
Age And Gender Classification | Adience Age | MegaAge | http://arxiv.org/abs/1708.09687v2 | Accuracy (5-fold) | 56.01 |
Age And Gender Classification | Adience Age | Levi_Hassner CNN (over-sample, caffe) | https://talhassner.github.io/home/publication/2015_CVPR | Accuracy (5-fold) | 50.7 |
Age And Gender Classification | Adience Age | Levi_Hassner CNN (single crop, caffe) | https://talhassner.github.io/home/publication/2015_CVPR | Accuracy (5-fold) | 49.5 |
Age And Gender Classification | Adience Age | LMTCNN-2-1 (single crop, tensorflow) | http://arxiv.org/abs/1806.02023v1 | Accuracy (5-fold) | 44.26 |
Age And Gender Classification | Adience Age | Levi_Hassner CNN (single crop, tensorflow) | https://talhassner.github.io/home/publication/2015_CVPR | Accuracy (5-fold) | 44.14 |
Drawing Pictures > Style Transfer | StyleBench | StyleShot | https://arxiv.org/abs/2407.01414v1 | CLIP Score | 0.660 |
Drawing Pictures > Style Transfer | StyleBench | StyleID | https://arxiv.org/abs/2312.09008v2 | CLIP Score | 0.604 |
Drawing Pictures > Style Transfer | StyleBench | StrTR-2 | http://openaccess.thecvf.com//content/CVPR2022/html/Deng_StyTr2_Image_Style_Transfer_With_Transformers_CVPR_2022_paper.html | CLIP Score | 0.586 |
Drawing Pictures > Style Transfer | StyleBench | CAST | https://arxiv.org/abs/2205.09542v2 | CLIP Score | 0.575 |
Drawing Pictures > Style Transfer | StyleBench | AdaAttN | https://arxiv.org/abs/2108.03647v2 | CLIP Score | 0.569 |
Drawing Pictures > Style Transfer | StyleBench | InST | https://arxiv.org/abs/2211.13203v3 | CLIP Score | 0.569 |
Drawing Pictures > Style Transfer | StyleBench | EFDM | https://arxiv.org/abs/2203.07740v2 | CLIP Score | 0.561 |
Drawing Pictures > Style Transfer | 01/01/1967' AND 2*3*8=6*8 AND 'AncJ'='AncJ | Ali | https://arxiv.org/abs/2402.04499v2 | 0..5sec | Download |
Drawing Pictures > Style Transfer | GYAFC | BART (TextBox 2.0) | https://arxiv.org/abs/2212.13005v1 | BLEU-4 | 76.93 |
Drawing Pictures > Style Transfer | GYAFC | BART (TextBox 2.0) | https://arxiv.org/abs/2212.13005v1 | Accuracy | 94.37 |
Drawing Pictures > Style Transfer | GYAFC | BART (TextBox 2.0) | https://arxiv.org/abs/2212.13005v1 | Harmonic mean | 84.74 |
Drawing Pictures > Style Transfer | ^(#$!@#$)(()))****** | studio Ghibli | https://arxiv.org/abs/2206.09379v2 | 0..5sec | studio Ghibli |
Drawing Pictures > Style Transfer | WikiArt | StyleFlow-Content-Fixed-I2I | https://arxiv.org/abs/2207.01909v1 | SSIM | 0.45 |
Drawing Pictures > Style Transfer | WikiArt | Mamba-ST | https://arxiv.org/abs/2409.10385v1 | ArtFID | 27.11 |
Blind Image Deblurring > Deblurring | . | 1 | https://arxiv.org/abs/2201.09302v1 | 10 Images, 4*4 Stitching, Exact Accuracy | 2 |
Blind Image Deblurring > Deblurring | RealBlur-J | AdaRevD | https://arxiv.org/abs/2406.09135v1 | SSIM (sRGB) | 0.944 |
Blind Image Deblurring > Deblurring | RealBlur-J | AdaRevD | https://arxiv.org/abs/2406.09135v1 | PSNR (sRGB) | 33.96 |
Blind Image Deblurring > Deblurring | RealBlur-J | MLWNet | https://arxiv.org/abs/2401.00027v2 | SSIM (sRGB) | 0.941 |
Blind Image Deblurring > Deblurring | RealBlur-J | MLWNet | https://arxiv.org/abs/2401.00027v2 | PSNR (sRGB) | 33.84 |
Blind Image Deblurring > Deblurring | RealBlur-J | ID-Blau (Stripformer) | https://arxiv.org/abs/2312.10998v2 | SSIM (sRGB) | 0.940 |
Blind Image Deblurring > Deblurring | RealBlur-J | ID-Blau (Stripformer) | https://arxiv.org/abs/2312.10998v2 | PSNR (sRGB) | 33.77 |
Blind Image Deblurring > Deblurring | RealBlur-J | ID-Blau (Stripformer) | https://arxiv.org/abs/2312.10998v2 | Params(M) | 20 |
Blind Image Deblurring > Deblurring | RealBlur-J | ID-Blau (Restormer) | https://arxiv.org/abs/2312.10998v2 | SSIM (sRGB) | 0.937 |
Blind Image Deblurring > Deblurring | RealBlur-J | ID-Blau (Restormer) | https://arxiv.org/abs/2312.10998v2 | PSNR (sRGB) | 33.11 |
Blind Image Deblurring > Deblurring | RealBlur-J | ALGNet | https://arxiv.org/abs/2403.20106v2 | SSIM (sRGB) | 0.946 |
Blind Image Deblurring > Deblurring | RealBlur-J | ALGNet | https://arxiv.org/abs/2403.20106v2 | PSNR (sRGB) | 32.94 |
Blind Image Deblurring > Deblurring | RealBlur-J | LoFormer | https://arxiv.org/abs/2407.16993v1 | SSIM (sRGB) | 0.933 |
Blind Image Deblurring > Deblurring | RealBlur-J | LoFormer | https://arxiv.org/abs/2407.16993v1 | PSNR (sRGB) | 32.90 |
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